Low bit-rate coding of image sequences using adaptive regions of interest

Doulamis, Nikolaos; Doulamis, Anastasios; Kalogeras, Dimitris; Kollias, Stefanos

doi:10.1109/76.736718

Cited by 99 publications

(49 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…While our primary interest is to use ROI-based bit allocation as a demonstration of the applicability of eye gaze prediction, the availability of real-time eye-gaze information does provide a firm basis for determination of ROI. In contrast, prior research without eye gaze information has to rely solely on video analysis such as high frequency content [38] and motion content [18], with the aforementioned saliency map also a suitable candidate.…”

Section: Roi-based Bit Allocation For Video Coding / Streamingmentioning

confidence: 91%

“…The idea of preferentially allocating more resources to a region of interest during video encoding is not new [18,19,38]. While our primary interest is to use ROI-based bit allocation as a demonstration of the applicability of eye gaze prediction, the availability of real-time eye-gaze information does provide a firm basis for determination of ROI.…”

Section: Roi-based Bit Allocation For Video Coding / Streamingmentioning

confidence: 99%

See 1 more Smart Citation

Low-Cost Eye Gaze Prediction System for Interactive Networked Video Streaming

Feng

Cheung

Tan

et al. 2013

IEEE Trans. Multimedia

View full text Add to dashboard Cite

Eye gaze is now used as a content adaptation trigger in interactive media applications, such as customized advertisement in video, and bit allocation in streaming video based on region-of-interest (ROI). The reaction time of a gaze-based networked system, however, is lower-bounded by the network round trip time (RTT). Furthermore, only low-sampling-rate gaze data is available when commonly available webcam is employed for gaze tracking. To realize responsive adaptation of media content even under non-negligible RTT and using common low-cost webcams, we propose a Hidden Markov Model (HMM) based gaze-prediction system that utilizes the visual saliency of the content being viewed. Specifically, our HMM has two states corresponding to two of human's intrinsic gaze behavioral movements, and its model parameters are derived offline via analysis of each video's visual saliency maps. Due to the strong prior of likely gaze locations offered by saliency information, accurate runtime gaze prediction is possible even under large RTT and using common webcam.We demonstrate the applicability of our low-cost gaze prediction system by focusing on ROI-based bit allocation for networked video streaming. To reduce transmission rate of a video stream without degrading viewer's perceived visual quality, we allocate more bits to encode the viewer's current spatial ROI, while devoting fewer bits in other spatial regions. The challenge lies in overcoming the delay between the time a viewer's ROI is detected by gaze tracking, to the time the effected video is encoded, delivered and displayed at the viewer's terminal. To this end, we use our proposed low-cost gaze prediction system to predict future eye gaze locations, so that optimized bit allocation can be performed for future frames. Through extensive subjective testing, we show that bit-rate can be reduced by up to 29% without noticeable visual quality degradation when RTT is as high as 200ms.

show abstract

Section: Roi-based Bit Allocation For Video Coding / Streamingmentioning

confidence: 91%

Section: Roi-based Bit Allocation For Video Coding / Streamingmentioning

confidence: 99%

Low-Cost Eye Gaze Prediction System for Interactive Networked Video Streaming

Feng

Cheung

Tan

et al. 2013

IEEE Trans. Multimedia

View full text Add to dashboard Cite

show abstract

“…A neural network-based approach is employed in [16] to determine foreground and background blocks. In [17] the Sarnoff Visual Discrimination Model is introduced to detect the Just Noticeable Difference (JND) by taking into account several parameters such as the distance from the observer to the image plane, the eccentricity of the image in the observer's visual field and the eye's point spread function.…”

Section: Roi Detectionmentioning

confidence: 99%

Region of Interest Coding for Aerial Video Sequences Using Landscape Models

Meuel¹,

Schmidt²,

Munderloh³

et al. 2013

Advanced Video Coding for Next-Generation Multimedia Services

View full text Add to dashboard Cite

“…Top-down attention has been more popularly employed because of its intuitiveness. Frequently used top-down cues include faces [20], [21], skin regions [22], [23], moving objects [3], [24], etc. Bottom-up and top-down attention models can be combined for more accurate attention modeling.…”

Section: B Foveated Video Codingmentioning

confidence: 99%

Subjective Quality Evaluation of Foveated Video Coding Using Audio-Visual Focus of Attention

Lee

Simone

Ebrahimi

2011

IEEE J. Sel. Top. Signal Process.

View full text Add to dashboard Cite

Abstract-This paper presents a foveated coding method using audio-visual focus of attention and its evaluation through extensive subjective experiments on both standard-definition and high-definition sequences. Regarding a sound-emitting region as the location drawing the human attention, the method applies varying quality levels in an image frame according to the distance of a pixel to the identified sound source. Two experiments are presented to prove the efficiency of the method. Experiment 1 examines the validity and effectiveness of the method in comparison to the constant quality coding for high-quality conditions. In Experiment 2, the method is compared to the fixed bit rate coding for low quality conditions where coding artifacts are noticeable. The results demonstrate that the foveated coding method provides considerable coding gain without significant quality degradation, but uneven distributions of the coding artifacts (blockiness) by the method are often less preferred than the uniform distribution of the artifacts. Additional interesting findings are also discussed, such as content dependence of the performance of the method, the memory effect in multiple viewings, and the difference in the quality perception for frame size variations.Index Terms-Audio-visual focus of attention, content dependence, foveated coding, H.264/AVC, memory effect, quality of experience, subjective quality assessment.

show abstract

Low bit-rate coding of image sequences using adaptive regions of interest

Cited by 99 publications

References 6 publications

Low-Cost Eye Gaze Prediction System for Interactive Networked Video Streaming

Low-Cost Eye Gaze Prediction System for Interactive Networked Video Streaming

Region of Interest Coding for Aerial Video Sequences Using Landscape Models

Subjective Quality Evaluation of Foveated Video Coding Using Audio-Visual Focus of Attention

Contact Info

Product

Resources

About